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題名 應用社會網路分析於易經爻辭之文字特徵觀察
Application of Social Network Analysis For Text Characteristic Observation On I-Ching Line Statements
作者 李俊澔
Lee, Chun Hao
貢獻者 劉吉軒
Liu, Jyi Shane
李俊澔
Lee, Chun Hao
關鍵詞 易經爻辭
詞頻分析
社會網路分析
資料分析
I Ching Line Statements
Word Frequency Analysis
Social Network Analysis
Data Analysis
日期 2016
上傳時間 22-Aug-2016 11:06:55 (UTC+8)
摘要 隨著資訊技術的進步，各種史料文本的數位化工作已經處理完成，運用資訊技術於史料文本分析的研究日益增加。本研究以詞頻分析與社會網路分析為主軸，對於古代《易經》爻辭的文字進行多元化的觀察，本研究首先以詞頻分析探討《易經》爻辭字詞頻率的觀察，再利用《易經》爻辭位置資訊建構成各個社會網路結構，對每個社會網路結構運算各項社會網路指標數據，最後將實驗結果與過往《易經》爻辭的論點做印證與對照，期望對於《易經》爻辭之分析，有更多元性的客觀研究觀察。本研究提供了一個分析《易經》爻辭的新面向，也可供未來研究者對於其他古文研究作參考。
With advances in information technology, digitization of various historical text has been completed.The study of historical text analysis by using information technology is in-creasing daily.In this paper, we used word frequency analysis and social network analy-sis in the I-Ching line statements.First, we used word frequency analysis in I-Ching line statements,using N-gram and TF-IDF technique analysis word frequency.Second, we constructed social network structure by I-Ching line statements position infor-mation,calculating several social network analysis indicator on each network.We com-pared our experiment results with some existing I-Ching theory, expecting to get more objective results and more diverse analysis for the I-Ching line statements. We not only provided a new perspective to study I-Ching line statements but also expected to help other researchers to study different historical text.
參考文獻 [1] Chen, S.-P., et al., On building a full-text digital library of historical documents, in Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers. 2007, Springer. p. 49-60.
[2] Sturgeon（德龍）, D. 中國哲學書電子化計劃（Chinese Text Project）. 2011; Available from: http://ctext.org.
[3] 項潔、涂豐恩, 導論—什麼是數位人文, in 從保存到創造: 開啟數位人文研究. 2011. p. 9-28.
[4] Manning, C.D., P. Raghavan, and H. Schütze, Introduction to information retrieval. Vol. 1. 2008: Cambridge university press Cambridge.
[5] Han, J., M. Kamber, and J. Pei, Data mining: concepts and techniques: concepts and techniques. 2011: Elsevier.
[6] 金觀濤、邱偉雲、劉昭麟, 「共現」詞頻分析及其運用:以「華人」觀念起源為例, in 數位人文要義 : 尋找類型與軌跡, 項潔, Editor. 2012, 臺灣大學出版中心. p. 141-170.
[7] Edmonds, P. Choosing the word most typical in context using a lexical co-occurrence network. in Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. 1997. Association for Computational Linguistics.
[8] Scott, J., Social network analysis. 2012: Sage.
[9] 劉吉軒、柯雲娥、張惠真、譚修雯、黃瑞期、甯格致, 以文本分析呈現臺灣海外史料政治思想輪廓, in 數位人文要義 : 尋找類型與軌跡, 項潔, Editor. 2012, 臺灣大學出版中心. p. 83-116.
[10] 張善文, 歷代易學要籍解題. 2006, 頂淵文化.
[11] 鄭吉雄, 從卦爻辭字義的演繹論《 易傳》 對《 易經》 的詮釋, in 漢學研究. 2006. p. 1-33.
[12] 陳伯适, 李道平《周易集解纂疏》的爻位「當」、「應」觀析論 , in 政大中文學報. 2009, 陳睿宏. p. 121-158.
[13] 陳威, 《 周易》 卦爻辭同文現象研究, in 臺灣師範大學國文學系學位論文. 2007. p. 1-128.
[14] Liu, C.-L., et al. Textual Analysis for Studying Chinese Historical Documents and Literary Novels. in Proceedings of the ASE BigData & SocialInformatics 2015. 2015. ACM.
[15] 徐志銳, 周易新譯. 1996: 里仁書局.
[16] 傅佩荣, 樂天知命: 傅佩榮談《 易經》. 2011: 天下遠見出版股份有限公司.
[17] 周文王, 周易新解. 2015: 華志文化事業有限公司.
[18] Feldman, R. and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. 2007: Cambridge University Press.
[19] Roberts, C.W., A conceptual framework for quantitative text analysis. Quality and Quantity, 2000. 34(3): p. 259-274.
[20] Carroll, J.M. and R. Roeloffs, Computer selection of keywords using word-frequency analysis. American Documentation (pre-1986), 1969. 20(3): p. 227.
[21] Pak, A. and P. Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. in LREC. 2010.
[22] Cavnar, W.B. and J.M. Trenkle, N-gram-based text categorization. Ann Arbor MI, 1994. 48113(2): p. 161-175.
[23] Jurafsky, D. and J.H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. MIT Press.
[24] Salton, G., Automatic text processing: The transformation, analysis, and retrieval of. Reading: Addison-Wesley, 1989.
[25] Chowdhury, G., Introduction to modern information retrieval. 2010: Facet publishing.
[26] Borgatti, S.P. and P.C. Foster, The network paradigm in organizational research: A review and typology. Journal of management, 2003. 29(6): p. 991-1013.
[27] Freeman, L.C., Centrality in social networks conceptual clarification. Social networks, 1979. 1(3): p. 215-239.
[28] Carrington, P.J., J. Scott, and S. Wasserman, Models and methods in social network analysis. Vol. 28. 2005: Cambridge university press.
[29] Carley, K.M., Network text analysis: The network position of concepts. Text analysis for the social sciences: Methods for drawing statistical inferences from texts and transcripts, 1997: p. 79-100.
[30] Diesner, J. and K.M. Carley, Revealing social structure from texts. Causal mapping for research in information technology, 2004: p. 81.
[31] Martin, M.K., J. Pfeffer, and K.M. Carley, Network text analysis of conceptual overlap in interviews, newspaper articles and keywords. Social Network Analysis and Mining, 2013. 3(4): p. 1165-1177.
[32] Hunter, S.D. and S. Smith, Center of Attention: A Network Text Analysis of American Sniper. 2015.
[33] Hunter, S. and S. Singh, A Network Text Analysis of Fight Club. Theory and Practice in Language Studies, 2015. 5(4): p. 737-749.
[34] Schütze, H. and J.O. Pedersen, A cooccurrence-based thesaurus and two applications to information retrieval. Information Processing & Management, 1997. 33(3): p. 307-318.
[35] Sudhahar, S., G.A. Veltri, and N. Cristianini, Automated analysis of the US presidential elections using Big Data and network analysis. Big Data & Society, 2015. 2(1): p. 2053951715572916.
[36] Özgür, A., B. Cetin, and H. Bingol, Co-occurrence network of reuters news. International Journal of Modern Physics C, 2008. 19(05): p. 689-702.
[37] Liang, W., et al., Co-occurrence network analysis of modern Chinese poems. Physica A: Statistical Mechanics and its Applications, 2015. 420: p. 284-293.
[38] Leydesdorff, L. and P. Zhou, Co‐word analysis using the Chinese character set. Journal of the American Society for information Science and Technology, 2008. 59(9): p. 1528-1530.
[39] Cohen, A.M., et al., Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts. BMC bioinformatics, 2005. 6(1): p. 103.
[40] Feicheng, M. and L. Yating, Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags. Online Information Review, 2014. 38(2): p. 232-247.
[41] Borgatti, S.P. and M.G. Everett, Models of core/periphery structures. Social networks, 2000. 21(4): p. 375-395.
[42] 李镜池, 周易筮辞续考. 周易探源, 1978.
描述 碩士
國立政治大學
資訊科學學系
101753035
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101753035
資料類型 thesis
dc.contributor.advisor 劉吉軒zh_TW
dc.contributor.advisor Liu, Jyi Shaneen_US
dc.contributor.author (Authors) 李俊澔zh_TW
dc.contributor.author (Authors) Lee, Chun Haoen_US
dc.creator (作者) 李俊澔zh_TW
dc.creator (作者) Lee, Chun Haoen_US
dc.date (日期) 2016en_US
dc.date.accessioned 22-Aug-2016 11:06:55 (UTC+8)-
dc.date.available 22-Aug-2016 11:06:55 (UTC+8)-
dc.date.issued (上傳時間) 22-Aug-2016 11:06:55 (UTC+8)-
dc.identifier (Other Identifiers) G0101753035en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100499-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 101753035zh_TW
dc.description.abstract (摘要) 隨著資訊技術的進步，各種史料文本的數位化工作已經處理完成，運用資訊技術於史料文本分析的研究日益增加。本研究以詞頻分析與社會網路分析為主軸，對於古代《易經》爻辭的文字進行多元化的觀察，本研究首先以詞頻分析探討《易經》爻辭字詞頻率的觀察，再利用《易經》爻辭位置資訊建構成各個社會網路結構，對每個社會網路結構運算各項社會網路指標數據，最後將實驗結果與過往《易經》爻辭的論點做印證與對照，期望對於《易經》爻辭之分析，有更多元性的客觀研究觀察。本研究提供了一個分析《易經》爻辭的新面向，也可供未來研究者對於其他古文研究作參考。zh_TW
dc.description.abstract (摘要) With advances in information technology, digitization of various historical text has been completed.The study of historical text analysis by using information technology is in-creasing daily.In this paper, we used word frequency analysis and social network analy-sis in the I-Ching line statements.First, we used word frequency analysis in I-Ching line statements,using N-gram and TF-IDF technique analysis word frequency.Second, we constructed social network structure by I-Ching line statements position infor-mation,calculating several social network analysis indicator on each network.We com-pared our experiment results with some existing I-Ching theory, expecting to get more objective results and more diverse analysis for the I-Ching line statements. We not only provided a new perspective to study I-Ching line statements but also expected to help other researchers to study different historical text.en_US
dc.description.tableofcontents 第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究資料 4
1.4 論文架構 5
第二章 文獻探討 6
2.1 文字探勘 (Text Mining) 6
2.2 詞頻分析(word frequency analysis) 6
2.3 社會網路分析(Social Network Analysis) 7
2.4 網路文字分析(Network Text Analysis) 7
2.5 共現網路(Co-occurrence network analysis) 8
第三章 研究方法與系統架構 9
3.1 研究流程架構 9
3.2 文本資料前處理 9
3.2.1 《易經》符號系統結構 10
3.2.2 《易經》爻位貴賤 10
3.2.3 《易經》爻辭時序性 11
3.2.4 爻辭與爻位規則條例 11
3.2.5 爻辭常見之重要名詞 12
3.3 詞頻分析 14
3.3.1 N-gram model 14
3.3.2 TF-IDF（term frequency–inverse document frequency）14
3.4 共現網路關聯定義 15
3.4.1 相鄰關係與標點符號分段定義關聯(relation) 15
3.4.2 全關係與跨標點符號定義關聯(relation) 16
3.5 社會網路分析 16
第四章 實驗數據與結果 20
4.1 詞頻分析 20
4.1.1 1-gram詞頻分析 20
4.1.2 1-gram TF-IDF 25
4.1.3 2-gram詞頻分析 30
4.1.4 2-gram TF-IDF 35
4.2 社會網路分析 39
4.2.1 連通子圖 39
4.2.2 社會網路分析 59
第五章 結論與未來研究方向 93
5.1 研究結論 93
5.2 未來研究方向 94
Reference 96		zh_TW
dc.format.extent 3003776 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101753035en_US
dc.subject (關鍵詞) 易經爻辭zh_TW
dc.subject (關鍵詞) 詞頻分析zh_TW
dc.subject (關鍵詞) 社會網路分析zh_TW
dc.subject (關鍵詞) 資料分析zh_TW
dc.subject (關鍵詞) I Ching Line Statementsen_US
dc.subject (關鍵詞) Word Frequency Analysisen_US
dc.subject (關鍵詞) Social Network Analysisen_US
dc.subject (關鍵詞) Data Analysisen_US
dc.title (題名) 應用社會網路分析於易經爻辭之文字特徵觀察zh_TW
dc.title (題名) Application of Social Network Analysis For Text Characteristic Observation On I-Ching Line Statementsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Chen, S.-P., et al., On building a full-text digital library of historical documents, in Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers. 2007, Springer. p. 49-60.
[2] Sturgeon（德龍）, D. 中國哲學書電子化計劃（Chinese Text Project）. 2011; Available from: http://ctext.org.
[3] 項潔、涂豐恩, 導論—什麼是數位人文, in 從保存到創造: 開啟數位人文研究. 2011. p. 9-28.
[4] Manning, C.D., P. Raghavan, and H. Schütze, Introduction to information retrieval. Vol. 1. 2008: Cambridge university press Cambridge.
[5] Han, J., M. Kamber, and J. Pei, Data mining: concepts and techniques: concepts and techniques. 2011: Elsevier.
[6] 金觀濤、邱偉雲、劉昭麟, 「共現」詞頻分析及其運用:以「華人」觀念起源為例, in 數位人文要義 : 尋找類型與軌跡, 項潔, Editor. 2012, 臺灣大學出版中心. p. 141-170.
[7] Edmonds, P. Choosing the word most typical in context using a lexical co-occurrence network. in Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. 1997. Association for Computational Linguistics.
[8] Scott, J., Social network analysis. 2012: Sage.
[9] 劉吉軒、柯雲娥、張惠真、譚修雯、黃瑞期、甯格致, 以文本分析呈現臺灣海外史料政治思想輪廓, in 數位人文要義 : 尋找類型與軌跡, 項潔, Editor. 2012, 臺灣大學出版中心. p. 83-116.
[10] 張善文, 歷代易學要籍解題. 2006, 頂淵文化.
[11] 鄭吉雄, 從卦爻辭字義的演繹論《 易傳》 對《 易經》 的詮釋, in 漢學研究. 2006. p. 1-33.
[12] 陳伯适, 李道平《周易集解纂疏》的爻位「當」、「應」觀析論 , in 政大中文學報. 2009, 陳睿宏. p. 121-158.
[13] 陳威, 《 周易》 卦爻辭同文現象研究, in 臺灣師範大學國文學系學位論文. 2007. p. 1-128.
[14] Liu, C.-L., et al. Textual Analysis for Studying Chinese Historical Documents and Literary Novels. in Proceedings of the ASE BigData & SocialInformatics 2015. 2015. ACM.
[15] 徐志銳, 周易新譯. 1996: 里仁書局.
[16] 傅佩荣, 樂天知命: 傅佩榮談《 易經》. 2011: 天下遠見出版股份有限公司.
[17] 周文王, 周易新解. 2015: 華志文化事業有限公司.
[18] Feldman, R. and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. 2007: Cambridge University Press.
[19] Roberts, C.W., A conceptual framework for quantitative text analysis. Quality and Quantity, 2000. 34(3): p. 259-274.
[20] Carroll, J.M. and R. Roeloffs, Computer selection of keywords using word-frequency analysis. American Documentation (pre-1986), 1969. 20(3): p. 227.
[21] Pak, A. and P. Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. in LREC. 2010.
[22] Cavnar, W.B. and J.M. Trenkle, N-gram-based text categorization. Ann Arbor MI, 1994. 48113(2): p. 161-175.
[23] Jurafsky, D. and J.H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. MIT Press.
[24] Salton, G., Automatic text processing: The transformation, analysis, and retrieval of. Reading: Addison-Wesley, 1989.
[25] Chowdhury, G., Introduction to modern information retrieval. 2010: Facet publishing.
[26] Borgatti, S.P. and P.C. Foster, The network paradigm in organizational research: A review and typology. Journal of management, 2003. 29(6): p. 991-1013.
[27] Freeman, L.C., Centrality in social networks conceptual clarification. Social networks, 1979. 1(3): p. 215-239.
[28] Carrington, P.J., J. Scott, and S. Wasserman, Models and methods in social network analysis. Vol. 28. 2005: Cambridge university press.
[29] Carley, K.M., Network text analysis: The network position of concepts. Text analysis for the social sciences: Methods for drawing statistical inferences from texts and transcripts, 1997: p. 79-100.
[30] Diesner, J. and K.M. Carley, Revealing social structure from texts. Causal mapping for research in information technology, 2004: p. 81.
[31] Martin, M.K., J. Pfeffer, and K.M. Carley, Network text analysis of conceptual overlap in interviews, newspaper articles and keywords. Social Network Analysis and Mining, 2013. 3(4): p. 1165-1177.
[32] Hunter, S.D. and S. Smith, Center of Attention: A Network Text Analysis of American Sniper. 2015.
[33] Hunter, S. and S. Singh, A Network Text Analysis of Fight Club. Theory and Practice in Language Studies, 2015. 5(4): p. 737-749.
[34] Schütze, H. and J.O. Pedersen, A cooccurrence-based thesaurus and two applications to information retrieval. Information Processing & Management, 1997. 33(3): p. 307-318.
[35] Sudhahar, S., G.A. Veltri, and N. Cristianini, Automated analysis of the US presidential elections using Big Data and network analysis. Big Data & Society, 2015. 2(1): p. 2053951715572916.
[36] Özgür, A., B. Cetin, and H. Bingol, Co-occurrence network of reuters news. International Journal of Modern Physics C, 2008. 19(05): p. 689-702.
[37] Liang, W., et al., Co-occurrence network analysis of modern Chinese poems. Physica A: Statistical Mechanics and its Applications, 2015. 420: p. 284-293.
[38] Leydesdorff, L. and P. Zhou, Co‐word analysis using the Chinese character set. Journal of the American Society for information Science and Technology, 2008. 59(9): p. 1528-1530.
[39] Cohen, A.M., et al., Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts. BMC bioinformatics, 2005. 6(1): p. 103.
[40] Feicheng, M. and L. Yating, Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags. Online Information Review, 2014. 38(2): p. 232-247.
[41] Borgatti, S.P. and M.G. Everett, Models of core/periphery structures. Social networks, 2000. 21(4): p. 375-395.
[42] 李镜池, 周易筮辞续考. 周易探源, 1978.		zh_TW